Computer vision

Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.

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arXiv AI

Jul 7

Fusion: A Framework for Unified Sequential Token AdaptatIon in VisiOn TraNsformers

arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.

By Aravind Pradeep, Samira Nazari, Mahdi Taheri, Christian Herglotz

llms computer-vision efficiency benchmarks

arXiv AI

Jul 7

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation

arXiv:2607. 03068v1 Announce Type: cross Abstract: Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful confidence filtering.

By Ebenezer Tarubinga

rag computer-vision

arXiv AI

Jul 7

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI

arXiv:2607. 03568v1 Announce Type: cross Abstract: Objective: Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events.

By Hasan Ulutas, Muhammet Emin Sahin, Mustafa Fatih Erkoc, Esra Yuce, Turker Tuncer, Sengul Dogan, Serkan Kiranyaz

diffusion computer-vision benchmarks

arXiv AI

Jul 7

Efficient Decentralized Multi-task Dataset Valuation via Model Merging

arXiv:2607. 03346v1 Announce Type: cross Abstract: Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models.

By Mohammadsajad Alipour, Mohammad Mohammadi Amiri

computer-vision nlp safety

arXiv AI

Jul 7

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

arXiv:2607. 03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance.

By Ivica Kopriva, Dario Sitnik, Arijana Pacic, Karolina Krstanac, Irena Veliki Dalic, Marijana Popovic Hadzija

computer-vision

arXiv AI

Jul 7

SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation

arXiv:2607. 03103v1 Announce Type: cross Abstract: Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks.

By Jiahao Liu, Hang Wei, Shuai Wu

diffusion computer-vision fine-tuning benchmarks safety

arXiv AI

Jul 7

Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.

By Ryosei Tamura, Andrew Shin

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 7

PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification

arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Mohammad Shafiul Alam, Ahmed Al Wase, Md. Rabius Sani, Khan Md Hasib

diffusion computer-vision safety

arXiv AI

Jul 7

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

arXiv:2607. 02636v1 Announce Type: cross Abstract: Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environments, infrastructure monitoring and defense applications.

By Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria

computer-vision robotics safety

arXiv Machine Learning

Jul 7

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.

By Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan

rag computer-vision safety

arXiv Machine Learning

Jul 7

SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers

arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.

By Jianing Deng, Yuanzhe Li, Jialu Wang, Song Wang, Tianlong Chen, Huanrui Yang, Jingtong Hu

llms computer-vision efficiency

arXiv AI

Jul 7

RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation

arXiv:2607. 05035v1 Announce Type: cross Abstract: Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation.

By Dongyi He, Xiangkai Wang, Binbing Xu, Bin Jiang, Hongjie Yan, Weixiang Liu, Wai Ting Siok, Nizhuan Wang

computer-vision benchmarks

arXiv AI

Jul 7

FM-ChangeNet: Learning Change through Pathwise Feature Transport

arXiv:2607. 04750v1 Announce Type: new Abstract: We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison.

By Roie Kazoom, George Leifman, Genady Beryozkin

computer-vision benchmarks safety

arXiv AI

Jul 7

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

arXiv:2601. 22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.

By Baorui Ma, Jiahui Yang, Donglin Di, Xuancheng Zhang, Jianxun Cui, Hao Li, Yan Xie, Wei Chen

llms computer-vision multimodal benchmarks

arXiv AI

Jul 7

HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy

arXiv:2607. 04353v1 Announce Type: cross Abstract: Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups.

By Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Anuun Chinbat, Hannes Leonhard, Moritz Ladenburger, Frederik Alexander, Vishisht Choudhary, Fabio Laredo, Giacomo Masserdotti, Thorben Prein, Carsten Marr, Amirhossein Kardoost

rag diffusion computer-vision efficiency

arXiv AI

Jul 7

From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

arXiv:2607. 02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.

By Francesco Fabbri, Martino Andrea Scarpolini, Paolo Ciancarella, Francesco Tudisco, Roberto Verzicco, Alessandro Ricci, Francesco Viola

diffusion computer-vision

arXiv AI

Jul 7

OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers

arXiv:2607. 04033v1 Announce Type: cross Abstract: Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, and task diversity, yet the landscape of over one hundred methods remains fragmented.

By Siyuan Li, Jiabao Pan, Yumou Liu, Zhuoli Ouyang, Xin Jin, Xinglong Xu, Jingxuan Wei, Shengye Pang, Jintao Che, Xuanhe Zhou, Conghui He, Cheng Tan

llms computer-vision benchmarks

arXiv AI

Jul 7

Double-Helix Active Geometry: LiDAR-Anchored Multi-View Depth with Selective Abstention

arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.

By Jinwen Wen

computer-vision benchmarks safety

arXiv AI

Jul 7

An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification

arXiv:2607. 03593v1 Announce Type: cross Abstract: Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning.

By Chengkun Sun, Jinqian Pan, Renjie Liang, Zhengkang Fan, Xin Miao, Yi Guo, Mei Liu, Muxuan Liang, Russell Terry, Jie Xu

computer-vision safety

arXiv AI

Jul 7

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting

arXiv:2607. 02637v1 Announce Type: cross Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models.

By Disheng Liu, Tuo Liang, Chaoda Song, Yu Yin

llms computer-vision fine-tuning benchmarks safety